Ethics, Governance, and Laws of Data Science, Artificial Intelligence, and Creative Systems

Harvard Extension School

CSCI E-184

Section 1

CRN 17472

View Course Details
Data science, artificial intelligence (AI), and creative systems are transforming how organizations make decisions, generate content, and interact with the world. These technologies operate across a full stack—from data collection and model development to applications that influence human behavior and societal outcomes—and introduce complex ethical, legal, and governance challenges. At the data and model layers, issues such as bias, privacy, and lack of transparency can shape downstream outcomes. At the application layer, AI-powered and generative systems may produce misleading, biased, or unauthorized content. At the decision and impact layers, these systems affect individuals, organizations, and society, raising questions of accountability, responsibility, and risk. This course examines ethical principles, governance frameworks, and legal considerations across the AI stack. Through case studies, simulations, and analysis of current events, students evaluate trade-offs, assess risks, and develop practical approaches for responsible design, deployment, and oversight of data-driven and AI-powered systems. Topics include fairness, interpretability, privacy, security, accountability, and the societal implications of predictive and generative technologies. The course prepares students to critically evaluate and govern systems that predict, decide, and create in real-world contexts. The course is designed for students across data science, computer science, and digital media disciplines who seek to build and govern responsible AI-driven and creative systems.

Instructor Info

Bruce Huang, EdD

Director of Master's Degree Program in Information Technology, Harvard Extension School


Meeting Info

M 11:00am - 1:00pm (8/31 - 12/19)

Participation Option: Online Asynchronous or Online Synchronous

In online asynchronous courses, you are not required to attend class at a particular time. Instead you can complete the course work on your own schedule each week.

Deadlines

Last day to register:

Notes

This course meets via web conference. Students may attend at the scheduled meeting time or watch recorded sessions asynchronously. Recorded sessions are typically available within a few hours of the end of class and no later than the following business day. See minimum technology requirements.

All Sections of this Course

CRN Section # Participation Option(s) Instructor Section Status Meets Term Dates
26606 1 Online Asynchronous, Online Synchronous Bruce Huang Open T 5:10pm - 7:10pm
Jan 24 to May 14
35565 1 Online Asynchronous, Online Synchronous Bruce Huang Open MW 3:15pm - 6:15pm
Jun 21 to Aug 6
17472 1 Online Asynchronous, Online Synchronous Bruce Huang Open M 11:00am - 1:00pm
Aug 30 to Dec 18